Cyber Threat Attenuation Using Multi-source Threat Data Analysis

ABSTRACT

A cyber threat attenuation system. The system comprises a cyber threat data store, a plurality of sensor control points (SCPs), wherein at least one SCP is located in each local area network (LAN) segment of an enterprise network, and an analytics correlation system (ACS). Each SCP comprises a plurality of sensor applications that analyze data packets transported by the LAN segment in which the SCP is located and transmits a notification identifying the transmitting sensor, an identity of the source of the data packet, an identity of the destination of the data packet, and a notification reason to the data store. The ACS comprises an application that determines unusual data packet traffic in the enterprise network and transmits a notification comprising information about the unusual data packet traffic and an identity of a host computer associated with the unusual data packet traffic to the data store.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority under 35U.S.C. § 120 to U.S. patent application Ser. No. 14/943,973 filed onNov. 17, 2015, entitled “Cyber Threat Attenuation Using Multi-SourceThreat Data Analysis,” by Michael Weinberger, et al., which isincorporated herein by reference in its entirety for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

REFERENCE TO A MICROFICHE APPENDIX

Not applicable.

BACKGROUND

We hear of major cyber crimes being committed too often. Large retailoutlets are hacked, credit cards are exposed, millions of dollars inlosses are sustained by financial institutions. In addition to readilyquantifiable monetary losses, the brand values of enterprises that havebeen victimized by a highly publicized cyber attack may suffer damage.Government organizations are being hacked and masses of confidentialinformation are pilfered. Cyber attacks and cyber criminals are becomingincreasingly sophisticated and the stakes in play are increasing invalue.

SUMMARY

In an embodiment, a cyber threat attenuation system is disclosed. Thecyber threat attenuation system comprises a cyber threat data store, anetwork data traffic data store that stores expected values of datatraffic volumes and measures of data traffic volume variabilities forhosts in each of a plurality of LAN segments of an enterprise networkand stores host data traffic volume information, and a user interface.The system further comprises a plurality of sensor control points(SCPs), wherein at least one SCP is located in each local area network(LAN) segment of the enterprise network, where each LAN segment islocated inside a firewall connection of the enterprise network to anexternal network. Each SCP comprises a first processor, a firstnon-transitory memory, and a plurality of sensor applications stored inthe first non-transitory memory that each, when executed by theprocessor, analyzes data packets transmitted on the LAN segment in whichthe SCP is located based on criterion identified by the sensor and,responsive to a data packet satisfying the criterion, transmits anotification identifying the transmitting sensor, an identity of thesource of the data packet, an identity of the destination of the datapacket, and a notification reason to the cyber threat data store. Thesystem further comprises an analytics correlation system (ACS)comprising a second processor, a second non-transitory memory, a networkdata traffic flow sensor application stored in the second non-transitorymemory that, when executed by the processor, accesses from the networkdata traffic data store information on data packet traffic in theenterprise network, inside the firewall connection of the enterprisenetwork to the external network, during a monitoring period, analyzesthe data packet traffic information to determine, based on comparing thedata traffic to expected values of data traffic volumes and measures ofdata traffic volume variabilities, an unusual data packet trafficassociated with a host computer in the enterprise network, inside thefirewall connection of the enterprise network to the external network,and transmits a notification comprising information about the unusualdata packet traffic and an identity of the host computer associated withthe unusual data packet traffic to the cyber threat data store, and arules engine application stored in the second non-transitory memorythat, when executed by the processor, analyzes a plurality ofnotifications identifying a first host computer, wherein the first hostcomputer is one of the host computers in the enterprise network, insidethe firewall connection of the enterprise network to the externalnetwork, based on rules configured into the rules engine application,and responsive to the analysis, one of sandboxes an applicationexecuting on the first host computer, restricts operations accessible tothe application executing on the first host computer, suspends theapplication executing on the first host computer, or takes down thefirst host computer.

In another embodiment, a cyber threat attenuation system is disclosed.The system comprises a cyber threat data store, a threat list data storecomprising a list of threat entries, each entry identifying an externalhost computer located outside of the enterprise network and metadataabout the external host computer and the threat it poses, a userinterface, and a plurality of sensor control points (SCPs), wherein atleast one SCP is located in each local area network (LAN) segment of anenterprise network. Each SCP comprises a first processor, a firstnon-transitory memory, and a plurality of sensor applications stored inthe first non-transitory memory that each, when executed by theprocessor, analyzes data packets transported by the LAN segment in whichthe SCP is located based on criterion identified by the sensor and,responsive to a data packet satisfying at least the criterion, transmitsa notification identifying the transmitting sensor, an identity of thesource of the data packet, an identity of the destination of the datapacket, and a notification reason to the cyber threat data store,wherein one of the sensor applications comprises a threat listed hostsensor application. The system further comprises an analyticscorrelation system (ACS) comprising a second processor, a secondnon-transitory memory, a threat list application stored in the secondnon-transitory memory that, when executed by the second processor,configures a threat listed host sensor application in each of the SCPsbased on the entries in the threat list data store, and a rules engineapplication stored in the second non-transitory memory. When executed bythe processor, the rules engine application analyzes a plurality ofnotifications identifying a first host computer including a notificationfrom a threat listed host sensor application that identifies the firsthost computer as the destination for a data packet sent by or the sourcefor a data packet sent to the host computer identified in threat listhosted sensor application that created the notification, wherein thefirst host computer is one of the host computers in the enterprisenetwork, based on rules configured into the rules engine application,and responsive to the analysis, transmits an alarm to the userinterface.

In an embodiment, a method of attenuating cyber threats is disclosed.The method comprises collecting by a computer system information on datapacket traffic in an enterprise network during a plurality of monitoringperiods and determining by the computer system an expected data packetflow rate and a measure of data packet traffic flow variability for thedata packet flow rate for each of a plurality of host computers in theenterprise network based on the data packet traffic information. Themethod further comprises determining data packet flow rates in theenterprise network by a flow sensor application executing on thecomputer system and determining by the flow sensor application that adata packet flow rate of a first host computer of the plurality of hostcomputers in the enterprise network is excessive based on the expecteddata packet flow rate and the measure of data packet flow ratevariability associated with the first host computer. The method furthercomprises, in response to the excessive data packet flow rate of thefirst host computer, transmitting by the flow sensor application a firstnotification to a cyber threat data store, where the first notificationcomprises an identity of the flow sensor application as the sender, anidentity of the first host computer, and an identity of a notificationreason, reading a threat data list by a cyber threat list applicationexecuting on the computer system, where the threat data list comprises aplurality of entries, each entry identifying an external host computerlocated outside of the enterprise network and metadata about theexternal host computer and the threat it poses, and configuring a threatlisted host sensor application with threat list data from a threat listdata store, where the threat list data store comprises a list of threatentries, each entry identifying an external host computer locatedoutside of the enterprise network and metadata about the external hostcomputer and the threat it poses, wherein one of the entries identifiesa first external host computer. The method further comprises determiningby the threat listed host sensor application that the first externalhost computer sent a data packet to the first host computer or that thefirst host computer sent a data packet to the first external hostcomputer; responsive to determining a data packet sent between the firstexternal host computer and the first host computer, transmitting by thethreat listed host sensor application a second notification to the cyberthreat data store, where the second notification comprises an identityof the threat listed host sensor application, an identity of the firstexternal host computer, an identity of the first computer, metadataabout the first external computer and about the threat it poses, and anidentification of a notification reason; analyzing the notificationsidentifying the first host computer by a rules engine applicationexecuting on the computer system; and based on analyzing thenotifications identifying the first host computer, sending by the rulesengine application an alarm to a user interface.

These and other features will be more clearly understood from thefollowing detailed description taken in conjunction with theaccompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure, referenceis now made to the following brief description, taken in connection withthe accompanying drawings and detailed description, wherein likereference numerals represent like parts.

FIG. 1 is a block diagram of a communication system according to anembodiment of the disclosure.

FIG. 2 is a block diagram of a sensor control point (SCP) serveraccording to an embodiment of the disclosure.

FIG. 3A and FIG. 3B is a flow chart of a method according to anembodiment of the disclosure.

FIG. 4 is a block diagram of a computer system according to anembodiment of the disclosure.

DETAILED DESCRIPTION

It should be understood at the outset that although illustrativeimplementations of one or more embodiments are illustrated below, thedisclosed systems and methods may be implemented using any number oftechniques, whether currently known or not yet in existence. Thedisclosure should in no way be limited to the illustrativeimplementations, drawings, and techniques illustrated below, but may bemodified within the scope of the appended claims along with their fullscope of equivalents.

The present disclosure teaches a system for detecting and responding tocyber threats and/or on-going cyber attacks that are present within thenetwork domain of an enterprise. Throughout the disclosure it will beunderstood that the term cyber threat may also refer to an active oron-going cyber attack. Said in other words, the present disclosureteaches a system for detecting and responding to cyber threats that arepresent within the perimeter of the enterprise network domain and arepresent on a host computer connected to or a network node of theenterprise network domain. The system taught by the present disclosureis deployed within the enterprise network domain and is independent ofany firewall device that may connect the enterprise network domain tothe external network (e.g., the firewall is not part of the system). Butnote, the system may take action to remediate cyber threats presentwithin the enterprise network domain, in part, by commanding thefirewall to take some action, for example to block packets in-bound tothe enterprise network domain that are received from a suspect externalIP address. In an embodiment, the system is designed to effectivelyattenuate the vulnerability of the enterprise to an advanced persistentthreat (APT) type of cyber threat (hereinafter referred to concisely asthe APT) that is beginning to besiege cyber space. The system is alsodesigned to detect and respond to zero-day threats, where a zero-daythreat is a cyber threat that has never been seen before in its presentform or has not been seen until within about the last twenty-four hours.The system is also designed to detect and respond to cyber threats ofrogue users within the enterprise, for example a user that has systemaccess credentials but is misusing them. At the same time, it isunderstood that the system may effectively attenuate enterprisevulnerability to other cyber threats that are not identified above.

APTs are devised to mount an attack over an extended period of timerather than to attack immediately. APTs typically operate by stealthyprocesses to avoid detection, with a view to continue an attack over anextended period of time. For example, an APT may infiltrate anenterprise network domain, hide itself in the enterprise network domain,collect confidential data over an extended period of time, and graduallyexfiltrate (export to a host outside the enterprise network domain) theconfidential data over an extended period of time. APTs typically targetlarge enterprises, probably because the inherent complexity of largeenterprise network domains can be exploited by APTs to do their workwith stealth and because the rewards for successful attacks are larger.Additionally, the longer time scale and greater complexity involved maymake APT attacks on low value targets uneconomical from a return oninvestment point of view. APTs are difficult to detect using known andconventional cyber security tools.

The system taught herein uses two or more independent processes tomonitor the enterprise network domain and to generate notifications whencommunication events of interest are detected. Notifications may bereferred to as security event reports or security event logs in somecontexts. The notifications comprise information about the communicationevent and identify an internal host that is engaged—as a receiver orsender of a message and/or data—in the communication event.Notifications are stored in a notification data store. As newnotifications are generated, a rules engine determines an internal hostidentified in the new notification, accesses other notifications storedin the data store that identify the same internal host, and evaluates asecurity status of the internal host and/or the enterprise networkdomain based on an analysis of what the collection of notifications maymean. For example, the rules engine may analyze the notifications to seeif they fit one of a plurality of cyber attack patterns that the rulesengine has been configured to identify.

The rules engine may be configured with a plurality of rules each ofwhich is articulated to identify a general cyber attack behavior pattern(e.g., a cyber attack tactic). The rules may be created based onanalysis by software developers and/or cyber threat analysts ofpreviously observed cyber attacks. That analysis abstracts from thespecific characteristics of the historical attacks to discern generaltactics employed in those attacks. Countermeasures that address specificcyber threat signatures may be avoided by cyber criminals that alter thesignature of a cyber attack while continuing to employ the same generaltactics. By monitoring for the manifestation of general attack tacticsand/or behaviors rather than specific attack signatures, the disclosedsystem raises the level of difficulty for cyber criminals seeking toattack an enterprise network domain, challenging them to createdifferent tactics.

As an example, a first monitor process may determine that an internalhost has sent an HTTP message that contains a hard coded IP address,where that host has not previously requested a DNS look-up that returnedthat IP address. The first monitor process stores a first notificationin the data store identifying this event and identifying the internalhost. A second monitor process may determine that the internal host hastransmitted two standard deviations more data packets than the medianvolume of data packets that it sends during a defined period of time.The second monitor process stores a second notification in the datastore identifying this event and identifying the internal host. A thirdmonitor process may determine that a destination IP address in a datapacket transmitted by the internal host is the IP address of an externalhost that is on a list of identified cyber threat hosts. The thirdmonitor process stores a third notification in the data storeidentifying this event, identifying the internal host, and identifyingthe external host. When the three events are analyzed together, they areconsistent with an APT executing on the internal host that collects dataand transmits the data back to the host identified by the IP addressthat was hard coded in the HTTP message and that is identified on thelist of identified cyber threat hosts.

The rules engine may analyze the first notification in isolation and notdeem the information sufficient for taking action. When the secondnotification is generated, the rules engine is triggered to analyze thesecond notification in combination with the first notification. Again,the rules engine may not deem the collective information sufficient fortaking action. When the third notification is generated, the rulesengine is triggered to analyze the third notification in combinationwith the first notification and the second notification. In the contextof this collection of information, the rules engine may determine that acyber threat is likely present in the enterprise network domain. Said inother words, the rules engine may recognize that the three notificationsfit a predefined pattern that associates to a cyber threat. The patternmay be a cyber crime tactic of installing a malware in an enterpriseinternal host that will collect confidential enterprise information andtransmit that confidential information to an external host computerunder the control of cyber criminals. In response to its analysis, therules engine may generate an alarm message that it sends to aresponsible security analyst for action and/or may take direct automatedaction itself.

The first monitoring process may comprise a plurality of sensorcomponents or sensor applications that execute in sensor control point(SCP) servers, where at least one SCP is located in each local areanetwork (LAN) segment of the enterprise network domain. Each sensorapplication may monitor for a different kind of anomalous networkcommunication event, for example for a hard coded IP address in an HTTPmessage that is not associated with a preceding DNS lookup or, anotherexample, a HTTP PUT command that is not associated with a following HTTPGET command. A sensor application may be said to detect an anomalousnetwork communication event when an analyzed packet satisfies acriterion defined in the subject sensor application. When a sensorapplication detects an anomalous network communication event it maycreate a notification identifying the internal host or hosts that isinvolved in the event and an identity of the event type and store thisnotification in the data store.

The first monitoring process may be said to be directed to monitoringfor and/or identifying cyber threat behaviors manifested in the use ofcommunication protocols, for example by “sniffing” and/or analyzing inreal-time individual data packets transported on the enterprise networkdomain and making inferences based on sequences of communicationprotocol uses. In an embodiment, an SCP may monitor sensors located inone or more network segments (where a network segment may comprisemultiple LANs). Additionally, in an embodiment, an SCP may monitorsensors located in network segments in which the SCP itself is notlocated. It is understood that the sensor applications described hereinare not “sensors” in the classical sense of a device that transduces aphysical property to an electrical signal (e.g., a pressure sensor thattransduces a gas pressure to an electrical analog or digital value, atemperature sensor that transduces a temperature to an electrical analogor digital value, etc.) but rather software implemented components orapplications that analyze data packets such as IP datagrams, TCPdatagrams, and the like.

The second monitoring process may comprise one or more applications thatexecute on a server computer in the enterprise domain network thatanalyzes statistics about network traffic sent to and from each internalhost to characterize the network traffic flows statistically and comparethe current data packet traffic on the enterprise domain network tocustomary enterprise domain network data traffic. For example, measuresof customary traffic flow and metrics of variability may be determined.The measures of variability may provide a relative measure of how acurrent data packet traffic volume compares to customary volumes. It iscontemplated that a number of different statistical analysis algorithmsand statistical metrics may be used. In an embodiment, a median datatraffic and standard deviation of the data traffic may be calculated andused to evaluate the data traffic. In an embodiment, a P-value may becalculated and used to evaluate the data traffic. In an embodiment,other statistical metrics may be used to measure how much a data trafficvolume differs from a customary data traffic volume.

The second monitoring process may comprise a data traffic analysisapplication that determines expected data packet traffic volumes or flowrates and measures of data packet traffic volume or flow ratevariability. The second monitoring process may further comprise anenterprise network data flow sensor that executes on the server computerthat monitors the data packet traffic of each internal host andgenerates notifications on that traffic based on the traffic flowdifference from customary traffic volumes based on a measure ofvariability of the traffic volumes of each internal host. The secondmonitoring process may be said to monitor the network behavior ofinternal hosts.

The third monitoring process may comprise a threat list application. Athreat feed received from a source external to the enterprise domainnetwork is stored in a threat list data store. The threat feed comprisesentries or records that identify external hosts that are likely sourcesof cyber threats. The threat list data store entries each comprise anidentity of the external host (e.g., an IP address) and other metadatathat characterizes or describes the nature of the cyber threat linked tothat external host. The external hosts may be listed on a black list oron a grey list, where black listed hosts are considered high probabilitycyber threat sources and grey listed hosts are considered mediumprobability cyber threat sources. Alternatively, black listed hosts maybe considered to represent high severity cyber risks and grey listedhosts may be considered to represent medium severity cyber risks. Thethreat list application may instantiate a threat list sensor for eachidentified external host in the threat feed on each of the SCPs.Alternatively, the threat list application may instantiate a singlethreat list sensor on each of the SCPs, where the single threat listsensor is configured with the IP addresses of the black listed and greylisted external hosts. The threat list sensors monitor data traffic inthe enterprise network domain, and when a data packet comprises a sourceaddress or destination address containing the IP address in the threatlist sensor (e.g., a black listed or grey listed IP address), itgenerates a notification identifying the IP address of the threat listedexternal host, the identity of the internal host that is associated withthe data packet, a notification identity, and stores this notificationin the notification data store.

The system described in brief above can be effective at detecting andresponding to APTs, zero-day threats, rogue user threats, as well asother cyber threats. The system can be said to employ a multivariateapproach to detecting and responding to cyber threats, where a firstvariable or dimension of cyber threat information is provided by thefirst monitoring process, a second variable or dimension of cyber threatinformation is provided by the second monitoring process, and a thirdvariable or dimension of cyber threat information is provided by thethird monitoring process. The system is highly scalable andmaintainable. As new cyber threat vulnerabilities are identified, newsensors can be developed and deployed to the SCPs. As new insights intothe relationships among communication events are achieved and cyberthreat patterns are recognized, the rules engine can be extended byadding new rules encompassing the new insights. The sensitivity of therules to the analysis of notifications can be dialed up or down, basedon the level of comfort of the enterprise and/or the level of perceivedcyber vulnerability.

Turning now to FIG. 1, a system 100 is described. In an embodiment, thesystem 100 comprises an enterprise network domain 101, an externalnetwork 136, and a plurality of external hosts 138. The enterprisedomain 101 comprises a plurality of local area network (LAN) segments102, a cyber threat security computer system 112, a cyber threat datastore 124, a threat list data store 125, a plurality of work stations(WS) 128, a network data traffic statistics data store 129, and aninternal network 130. Each LAN segment 102 comprises one or moreinternal hosts 104 and at least one sensor control point (SCP) server106. The internal hosts 104 are computers that are used to support theoperations of the enterprise, business, government entity, ororganization. These may be desktop computers, workstations, emailservers, database management system servers, billing servers, printers,and other computing devices communicatively coupled to the LAN segment102. The security computer system 112 may be referred to as an analyticscorrelation system (ACS) and/or a control system and user interface(CSU) in some contexts. The ACS and the CSU, which are described in moredetail below, may be supported on a single physical host or server.Alternatively, the ACS and CSU may be supported on two or more physicalhosts or servers. In an embodiment, some or all of the functionality ofthe ACS and/or the CSU may be provided using a virtual serverenvironment, for example a cloud computing environment.

Each SCP server 106 comprises an SCP application. Each SCP 106 comprisesa plurality of sensors 108. Sensors 108 monitor data traffic on the LANsegment 102 to detect anomalous communication events, to generatenotifications 126 about the anomalous events, and to store thenotifications 126 in the data store 124. An anomalous communicationevent may be any communication event that matches a predefined criterionof a sensor 108. Sensors 108 are discussed further hereinafter. The SCPapplication launches, monitors, and manages the sensors 108 that executeon the SCP server 106. The term SCP 106 will be used herein to referboth to the SCP server (a physical host) and to the SCP application (asoftware application, computer program, and/or script that executes onthe SCP server).

An enterprise cyber security system taught by the present disclosurecomprises the SCPs 106 with the sensors 108; the security computersystem 112 with a rules engine 114, a flow sensor 116, a threat listapplication 118, a traffic analysis application 120, and a userinterface 122; the data store 124; the threat list data store 125; thenetwork data traffic statistics data store 129; and the WSs 128. Theenterprise cyber security system operates within the enterprise networkdomain 101 inside of a firewall 103 that connects the enterprise networkdomain 101 to the external network 136. Said in other words, thefirewall 103 is not part of the enterprise cyber security system taughtby the present disclosure and is not considered to be “inside” theenterprise network domain 101 but instead on its periphery or evenoutside of the enterprise network domain 101. But note, the enterprisecyber security system may take action to remediate cyber threats presentwithin the enterprise network domain 101, in part, by commanding thefirewall 103 to take some action, for example to block packets in-boundto the enterprise network domain 101 that are received from a suspectexternal IP address. In some contexts, the threat list application 118may be referred to as a cyber threat portal application.

The internal network 130 comprises electronic communication equipmentwithin the enterprise network domain 101 that promotes communicationwithin the enterprise network domain 101 and couples the enterprisenetwork domain 101 to the external network 136. The internal network130, for example, may comprise routers, switches, bridges, hubs,wireless access points, firewalls (i.e., different firewalls from thefirewall 103 on the periphery of the enterprise network domain 101), andthe like communication equipment. Some of the electronic communicationequipment comprising the internal network 130 may be considered to bepart of one or more LAN segments 102. For example, a bridge may becommunicatively coupled to a first LAN segment 102 and a second LANsegment 102, e.g., to communicatively bridge the two LAN segments.Additionally, for example, a hub may be located in a LAN segment 102 tocommunicatively couple the LAN segment 102 together. In an embodiment,some of the communication equipment comprising the internal network 130may comprise some security clients or security applications and hencemay be considered to be, at least in part, additional components of thecyber security system.

A LAN segment is a section of a LAN separated from other LAN segments bycommunication equipment such as a bridge, a router, or a switch. A hubor repeater does not separate different LAN segments but instead mayhelp to create a single LAN segment or to extend a single LAN segment.Data packets transmitted by a host on a first LAN segment need not beseen by hosts on a second LAN segment separated from the first LANsegment by a bridge, switch, or router (e.g., when the bridge, switch,or router does not pass these data packets to the second LAN segment).By contrast data packets transmitted by a host on the first LAN segmentare generally seen by all other hosts on the first LAN segment. LANsegmentation may improve the efficiency of data communication in anenterprise network domain by reducing packet collisions. LANsegmentation may also provide enhanced security under some conditions.

The cyber threat security computer system 112 may be referred to byother names, for example an analytics and correlation system (ACS)and/or a control system and user interface (CSU). In an embodiment thefunctions of the cyber threat security computer system 112 may beprovided on two or more computers. The computer system 112 comprises arules engine 114, an enterprise network data flow sensor 116, a threatlist application 118, a network data packet traffic analysis application120, and a user interface 122. The user interface 122 may provide afront-end or interface for the WSs 128 that are used by cyber threatanalysts in the enterprise to monitor the security of the enterprisenetwork domain 101, to review notifications 126, to receive alarms fromthe rules engine 114, and to administer the cyber security elements ofthe enterprise network domain 101. The WSs 128 may be used to configureelements of the cyber threat security computer system 112, for exampleseverities of alarms, priorities of handling alarms, and other aspects.In an embodiment, the WSs 128 may present a web page linked to the userinterface 122 that provides an interface to the security system.

In an embodiment, the rules may be stored in the rules engine 114.Alternatively, in an embodiment, the rules may be stored in a file ordata structure external to the rules engine 114, and the rules engine114 may read the file or data structure periodically or when it istriggered to analyze notifications 126 in the light of a newly creatednotification 126.

It is understood that in an embodiment, the components 114, 116, 118,120, 122 may be partitioned into more components or combined into two orfewer components. For example, in an embodiment, the rules engine 114and the threat list application 118 may be combined in a singleapplication. Alternatively, the rules engine 114 may be partitioned intotwo or more components and/or the threat list application 118 may bepartitioned into two or more components. Additionally, the computersystem 112 may execute other components, not shown, that provide othercentralized cyber security processing.

The sensors 108 are separate applications or executable softwarecomponents. Each sensor 108 is of a particular sensor type that isconfigured to sense a specific type of network communication event. Saidin other words, the sensors 108 are configured with one or morecriterion that is used to analyze data packets and/or data traffic onthe LAN segments 102 and/or the enterprise network domain 101. Some ofthe sensors 108 may identify specific communication protocol messagesequences and detect message sequences that manifest probable maliciouscommunication behaviors or tactics. When such a message sequence isdetected, the sensor 108 sends a notification 126 to the data store 124.It is noted that a notification does not mean that a cyber threat ispresent or that a cyber attack is underway. A notification may bethought of as being a clue or a puzzle piece. The notification may meannothing and may be insignificant on its own. When analyzed in thecontext of other notifications, however, the notification may contributeto the recognition of a pattern of events that betoken a cyber threat orcyber attack.

In an embodiment, the sensors 108 may comprise sensors from a pluralityof the following sensor types and other sensor types not describedherein. In an embodiment, the functionality of the sensors 108 may becategorized into (1) sensing time to live values in excess of athreshold, (2) sensing specific sequences or state transitions, (3)sensing sizes of data communication in excess of a threshold, (4)sensing predefined port numbers or ranges of port numbers, and (5)sensing predefined names or suffixes. Some individual sensors 108 mayprovide two of more of the functionalities identified above, for exampledifferent functionalities used to analyze communications based on acommon protocol. In an embodiment, the sensors 108 may be categorizedinto (1) sensors operating at the network layer, (2) sensors operatingat the transport layer, and (3) sensors operating at communicationlayers above the transport layer.

In an embodiment, the sensors 108 may be categorized into (1) sensorsthat detect communication events related to a target phase of a cyberthreat, (2) sensors that detect communication events related to areconnaissance phase of a cyber threat, (3) sensors that detectcommunication events related to a compromise phase of a cyber threat,(4) sensors that detect communication events related to a lateral pivotphase of a cyber threat, (5) sensors that detect communication eventsrelated to a collection phase of a cyber threat, and (6) sensors thatdetect communication events related to an exfiltration phase of a cyberthreat. Some of the sensors 108 may provide functionality that interactswith communication events related to a plurality of the cyber threatphases identified above.

A file transfer sensor is configured to examine file transfercommunication events and to generate notifications if file transferrules implemented by the sensor are violated. File transfer rules may bedefined independently for each of a plurality of different file transferevents and may relate to file size, file types, file names. Differentfile transfer events may comprise HTTP_GET, HTTP_POST, HTTP_PUT,FTP_GET, SMTP, SMB_READ, and SMB_WRITE. Some rules may triggergeneration of a notification when a file request results in a “file notfound” reply message, for example a FTP error 550 or a SMB “file notfound.”

A TCP (transport control protocol) misuse sensor is configured tomonitor TCP messages and to generate notifications when anomalous TCPmessage patterns are detected. If a TCP session does not terminatewithin a predefined time period (e.g., within a 2 hour time limit), theTCP misuse sensor may create an associated notification. If a TCPconnect attempt receives no reply, the TCP misuse sensor may create anassociated notification.

A stateful HTTP (hypertext transfer protocol) transaction sensor isconfigured to detect anomalous HTTP message sequences and to generate anassociated notification. For example, typically a HTTP POST request to atarget host is preceded by a corresponding HTTP GET to the same targethost. If a HTTP POST is detected that cannot be linked to acorresponding preceding HTTP GET, the stateful HTTP transaction sensorcreates an associated notification. As another example, if more than oneHTTP POST is sent without an intermediate HTTP GET, the stateful HTTPtransaction sensor creates an associated notification.

An ICMP (Internet Control Message Protocol) misuse sensor is configuredto monitor use of the ICMP protocol and to generate notifications whenanomalous ICMP use is detected. Misuse of the ICMP protocol may beassociated with various cyber crime reconnaissance activities, forexample host detection and network topology discovery, access controllist (ACL) detection, and protocol/port scanning. The ICMP misuse sensormay monitor ICMP traffic to detect ICMP packets that exceed a predefinedsize threshold, to detect ICMP packets of predefined disallowed types,to detect ICMP packets having a predefined combination of type and code,and to detect ICMP packets that comprise a time to live (TTL) value thatviolates a predefined value range. Typical TTL values may be in therange of seconds, for example 10 seconds or 30 seconds. A TTL value inan ICMP message in the range of hours, then, would be anomalous. In theevent of any of these ICMP communication events being detected, the ICMPmisuse sensor may create an associated notification.

An ARP (address resolution protocol) misuse sensor is configured tomonitor use of the ARP protocol and to generate notifications whenanomalous ARP use is detected. Misuse of the ARP protocol may beassociated with an attempt to program a switch in the enterprise networkdomain 101 in conjunction with a man-in-the-middle cyber attack. The ARPmisuse sensor monitors volumes of ARP inbound and outbound requests andresponses. Rates in excess of predefined thresholds are deemed anomalousby the ARP misuse sensor, and the ARP misuse sensor creates anotification when it detects an excess ARP volume.

A UDP (user datagram protocol) misuse sensor is configured to monitoruse of the UDP protocol and to generate notifications when anomalous UDPuse is detected. The UDP misuse sensor may generate notifications when aUDP message is sent or received by an internal host that identifies aport number less than 1024 (a privileged port) or that is directed to anexternal subnet. In an embodiment, the UDP misuse sensor sends anotification if the communication protocol associated with the UDPmessage is inconsistent with the port number but does not send anotification if the communication protocol and port number areconsistent (e.g., an FTP access on well known port 20 is consistent anda SNTP access on well known port 25 is consistent).

A DNS (domain name system) misuse sensor is configured to monitor use ofthe DNS functionality and to generate notifications when anomalous DNSuse is detected. Misuse of the DNS function may be associated with acommand and control cyber threat behavior and/or an exfiltration cyberthreat behavior. The DNS misuse sensor may create notifications when itdetects a DNS resolution request for a fully qualified domain name(FQDN) that is identified as a disallowed DNS name in a predefined list,when it detects a DNS resolution request for a FQDN that contains morethan one 64-byte component, when it detects a DNS resolution request fora FQND that has more than 4 components, when it detects a DNS resolutionrequest that has an anomalous TTL (e.g., a TTL that is unusually long),and when it detects a DNS response that returns an excessive amount ofdata when compared to a predefined threshold.

In an embodiment, the DNS misuse sensor (or another sensor or othersoftware component) may monitor establishment of new IP network flows(e.g., establishment of a socket directly to an IP address). When a newIP network flow is detected, the DNS misuse sensor determines whetherthe new connection was preceded by a corresponding DNS lookup. When DNSlookups are conducted, the results may be stored in a DNS cache in theenterprise network domain 101. If there is not a corresponding DNSresult in the DNS cache, the DNS misuse sensor may generate anotification. It has been observed that under some test conditions ahigh percentage of malware opens sockets to an IP address without firstperforming a DNS lookup.

An IP (Internet protocol) connection scanning sensor is configured tomonitor IP packets to analyze whether scanning is being attempted usingeither TCP (transmission control protocol) or UDP (universal datagramprotocol) packets. The IP connection scanning sensor tracks inbound andoutbound failed connection attempts and ICMP destination unreachableerror packets as a strategy for identifying a port scanning activity.The IP connection scanning sensor may generate a notification if thenumber of failed connection attempts from a single internal host isgreater than a predefined threshold. In an embodiment, the connectionattempt threshold may be defined relative to a time interval, forexample failed connection attempts per minute, per hour, per day, orsome other time interval. Some legitimate tools that may execute oninternal hosts 104 or communication equipment in the internal network130, for example nmap or network monitors, may use techniques that looklike port scanning. It is contemplated that the IP connection scanningsensor may be configured with a list of internal hosts 104 and/orcommunication equipment in the internal network 130 that are authorizedto perform port scanning and to suppress notification creation by the IPconnection scanning sensor for those listed internal hosts 104 andcommunication equipment.

An IP header analysis sensor is configured to monitor internal hostsconnecting to or being connected from one of a predefined list of portsand generates a notification when such a connection occurs. Thepredefined list of ports may be associated with an elevated frequency ofcyber attacks.

An SMB (server message block) misuse sensor is configured to monitor SMBcommunication events in the enterprise network domain 101 and togenerate a notification when high risk SMB messaging patterns aredetected by the sensor. SMB is a protocol that may be used for providingshared access to files, printers, and serial ports. Sometimes the SMBprotocol may be exploited to distribute malware. As an example,detection of an SMB create request file message comprising multiple .lnkfiles may be used by the SMB misuse sensor as a trigger for generating anotification.

A RDP (remote desktop protocol) misuse sensor is configured to monitorRDP communication events in the enterprise network domain 101 and togenerate a notification when high risk RDP messaging patterns aredetected by the sensor. RDP is a protocol that provides an interface toconnect to another computer over a network connection.

Notifications 126 may be created by sensors 108 and sent to the datastore 124. Notifications 126 are not typically sent to individual users,cyber analysts, or WSs 128 (notwithstanding, a mode of the WS 128 mayprovide a functionality for accessing and presenting notifications 126in response to received user input to the WS 128). It is observed thatgeneration of a notification 126, by itself, does not indicate that acyber attack is in progress or that an internal host 104 has beencompromised. A notification 126 may be thought of as a clue or puzzlepiece. A notification 126 is an observation of a communication eventthat desirably is interpreted in the context of other notifications 126and possibly other information not captured in notifications 126. Apattern of notifications 126 that does imply a cyber attack or acompromised internal host 104 may be identified by the rules engine 114that accordingly may generate an alarm and/or take automated action toattenuate the threat.

Notifications 126 may be of different types, depending on the source ofthe notification 126 (e.g., what sensor 108 generated the notification126) and/or depending on the communication event that resulted ingeneration of the notification 126. The structures and/or informationcontent of notifications 126 can vary based on type. Notifications 126comprise an identification of the notification type and a uniqueidentification of the specific notification instance (i.e., twodifferent instances of the same notification type would have differentnames or identifiers). Notifications 126 comprise an identity of atleast one internal host 104 that was engaged with the communicationevent that resulted in generation of the subject notification 126. Insome cases, a notification 126 may comprise identities of two or moreinternal hosts 104, for example when a first internal host 104 sends adata message to a second internal host 104 or a plurality of internalhosts 104 (e.g., a broadcast data message). Some notifications 126comprise an identity of at least one external host 138. Host identitiesmay be articulated by IP addresses, MAC addresses, and/or by domainnames and/or by other identification values.

Some notifications 126 may comprise additional supporting information.For example, a notification 126 may comprise information identifyingsizes, counts, port numbers, time to live values that are deemed dubiousor anomalous by the sensor 108 that generated the notification 126. Forexample, a notification 126 may comprise metadata about an external host138 identified by the threat list application 118 as discussed furtherhereinafter.

In an embodiment, at least one sensor 108 of each of the differentsensor types identified above executes on at least one SCP 106 in everyLAN segment 102. Said in other words, in an embodiment, every LANsegment 102 is monitored by (1) a file transfer sensor, (2) a TCP misusesensor, (3) a stateful HTTP transaction sensor, (4) an ICMP misusesensor, (5) an ARP misuse sensor, (6) a UDP misuse sensor, (7) a DNSmisuse sensor, (8) an IP connection scanning sensor, (9) an IP headeranalysis sensor, (10) an SMB misuse sensor, and (11) a RDP misusesensor. Further, it is contemplated that every LAN segment 102 may bemonitored by other sensors 108 of different sensor types, as newstrategies for identifying potential anomalous communication events inthe enterprise network domain 101 are determined. In an embodiment,every LAN segment 102 is monitored by at least four of the identifieddifferent types of sensors 108. In an embodiment, every LAN segment 102is monitored by at least three of the identified different types ofsensors 108. In an embodiment, every LAN segment 102 is monitored by atleast six of the identified different types of sensors 108.

In an embodiment, the SCPs 106 may execute on or be communicativelycoupled to a test access point (TAP) and/or a switch port analyzer(SPAN) in its corresponding LAN segment 102. The SCP 106 (i.e., the SCPapplication) launches sensors 108. The SCP 106 may monitor and restartsensors 108 that terminate. The SCPs 106 and/or sensors 108 may be saidto passively monitor network traffic on its corresponding LAN segment102. In some contexts, the monitoring of the enterprise network domain101 and generation of notifications 126 by the sensors 108 may bereferred to as enterprise network domain traffic monitoring or nettraffic monitoring.

The sensors 108 may provide LAN segment data traffic information to theSCP application or a dedicated data traffic information collectionapplication. The data traffic information may be collected in a varietyof different bins or collectors. The data traffic information may becollected in a different bin or collector for each host on the LANsegment 102. The data traffic information may be collected in adifferent bin or collector for each different host interface (a singlehost may have a plurality of host interfaces) on the LAN segment 102.The data traffic information may be collected in a different bin orcollector for each different communication protocol and for eachdifferent host or host interface. The SCP 106 periodically transmits thedata traffic information to the network data traffic statistics datastore 129, for example in the form of summaries or counts of datatraffic events. The SCP 106 may transmit the data traffic information tothe traffic statistics data store 129 about every 30 seconds, aboutevery minute, about every five minutes, about every ten minutes, aboutevery fifteen minutes, about every twenty minutes, or some other periodof time.

The traffic analysis application 120 may execute on the securitycomputer system 112 and analyzes the data traffic information sent tothe traffics statistics data store 129 by the SCPs 106. The trafficanalysis application 120 processes the statistics to determine expectedtraffic volumes or traffic flows and to determine measures of trafficvolume or traffic flow variability, dispersion, scatter, or spread. Theexpected traffic volumes may be analyzed according to variousstatistical algorithms. The expected traffic volumes may be expressed asmedian values, average values, or in another form. The measure oftraffic volume variability may be expressed as standard deviations,P-values, interquartile range, or in another form. The expected valueand measure of variability may be determined separately for every hostinterface and every communication protocol (i.e., a first expectedvalue, a first measure of variability for a first host interface for afirst communication protocol; a second expected value, a second measureof variability for the first host interface for a second communicationprotocol). For example, different expected values and measures ofvariability for a host interface may be determined for each of TCPtraffic, UDP traffic, HTTP traffic, DNS traffic, ARP traffic, FTPtraffic, and ICMP traffic on that host interface. In an embodiment, theexpected value and measure of variability may be calculated for each ofa plurality of different times of day and/or days of the week. Thus, thefirst expected value and the first measure of variability may apply forthe first host interface for the first communication protocol for noonon a weekday but a second expected value and a second measure ofvariability may apply for the first host interface for the firstcommunication protocol for 4 PM on a weekday.

The enterprise network data flow sensor 116 may execute on the securitycomputer system 112. The flow sensor 116 monitors data packet trafficvolumes in the enterprise network domain 101 by analyzing the datatraffic information transmitted to the traffic statistics data store 129by the SCPs 106 and comparing that current information to thestatistical expected traffic volumes and measures of traffic volumevariability determined by the traffic analysis application 120. Theenterprise network data flow sensor 116 generates notifications when atraffic flow exceeds a predefined threshold, for example varies from theexpected traffic volume by more than a predefined number of calculatedvariability. For example, a notification may be generated if a trafficvolume of a host interface for a given communication protocol exceedsthe corresponding expected value by more than 1.5 standard deviations or0.02 P-values or 0.05 P-values. In some embodiments, automated analysisof traffic flow volumes may be performed using linear regression and/ornaïve Bayes techniques.

Over time, the traffic volume statistical metrics (i.e., expectedtraffic volumes and measures of variability) may be recalculated oradapted based on rolling windows of time bins. Over time thecommunication behavior of an internal host 104 or communicationequipment may change—increase or decrease gradually—and thus it iscontemplated that the traffic statistics and distributions will adjustcorrespondingly.

The flow sensor 116 may compare observed data traffic volume tostatistical metrics in a number of different ways, in addition to thedirect comparison to an expected data traffic volume based on a measureof variability. The ratio of transmitted data to received data on thehost interface for the subject communication protocol may be compared toa median or normal ratio of transmitted to received data for the subjecthost interface for the subject communication protocol. The ratio of thenumber of transmit data flow instances to the number of receive dataflow instances on the host interface for the subject communicationprotocol may be compared to a median or normal ratio of transmit dataflow instances to receive data flow instances for the host interface forthe subject communication protocol. The traffic analysis application 120may calculate the expected ratios and measures of variability of theratios.

The threat list data store 125 is updated periodically and/orasynchronously with a threat feed from an external source, for exampledaily, weekly, and/or monthly. The threat feed comprises entriesidentifying external hosts 138 that are associated with cyber threats.The information in the threat feed may be organized into a threat list127 in the threat list data store 125. The entries in the threat list127 may identify the external hosts 138 by IP address and/or by domainname. The entries may further comprise metadata describing variousaspects or information about the subject external host 138. For example,the metadata may describe a presumed threat that the external host 138is deemed to have promulgated or be compromised by. The metadata maycomprise a narrative of a cyber attack that has been perpetrated, atleast in part, by the external host 138. The metadata may comprise anidentification of a geographical location of an origin or source of theassociated cyber threat (e.g., location of an origin or a source ofmalware) and/or of a geographical location of the black listed or greylisted host computer. The metadata may comprise an identification of anindustry sector that is targeted by the associated cyber threat. Themetadata may comprise identification of systems, software, firmware,and/or applications that are targeted by the associated cyber threat.The metadata may comprise identification of a date of the threat dataand/or an expiration date of the threat data.

The external hosts 138 may be listed on a black list or on a grey list,where black listed hosts are considered high probability cyber threatsources and grey listed hosts are considered medium probability cyberthreat sources. Notwithstanding, however, the black lists and grey listmay be expected to contain “false positives”—hosts that are not reallycompromised hosts may be contained in the lists. Alternatively, blacklisted hosts may be considered to represent high severity cyber risksand grey listed hosts may be considered to represent medium severitycyber risks.

The threat list application 118 may instantiate a threat list sensor foreach identified external host on the threat list 127 in the threat listdata store 125 on each of the SCPs 106. The threat list sensors may havean expiration time such that they terminate themselves after apredetermined time duration, for example a default time duration or atime duration specified in the metadata of the associated threat listentry. A time to live of a threat list sensor may be reset when a newthreat feed is received and the subject external host 138 is stillincluded in the refreshed threat list 127. The threat list application118 may refresh the time to live of threat list sensors when the threatfeed is received by the threat list data store 125 and the associatedexternal hosts 138 are still included in the thread feed.

Alternatively, the threat list application 118 may instantiate a singlethreat list sensor on each of the SCPs 106, where the single threat listsensor is configured with the IP addresses of the black listed and greylisted external hosts. In an embodiment, each black listed or greylisted external host configured in the threat list sensor may have atime to live value that is updated by the threat list application 118when the threat feed is received by the threat list data store 125. Whenthe time to live of a black listed or grey listed host expires, thesubject host may be removed from the threat sensor host list.Alternatively, if a notification 126 would otherwise be made in responseto observed data packet traffic related to a black listed or grey listedhost but the time to live of that host has expired, the notification 126is not created and not sent.

The threat list sensors monitor data traffic in the enterprise networkdomain 101, and when a data packet comprises a source address ordestination address containing the IP address in the threat list sensor(e.g., a black listed or grey listed IP address), it generates anotification 126 identifying the IP address of the threat listedexternal host 138, the identity of the internal host 104 that isassociated with the data packet, a notification identity, and storesthis notification 126 in the data store 124. In an embodiment, thenotification 126 generated by the threat list sensor may furthercomprise the metadata associated with the associated threat list entry.

The rules engine 114 analyzes the notifications 126 stored in the datastore 124 to identify a plurality of notifications 126 that conform to apredefined pattern indicative of a cyber threat or a malware presence inthe enterprise network domain 101. The creation and storing of a newnotification 126 in the data store 124 may trigger the rules engine 114to read the new notification 126, determine at least one internal host104 associated with the notification 126, search the data store 124 forall other notifications 126 that are associated with the same internalhost(s) 104, and analyze these notifications collectively in light ofpredefined criteria.

The rules engine 114 may execute a plurality of rules or criterionchecks against a set of notifications 126 associated with an internalhost 104, and if any criterion check or rule is satisfied, the rulesengine 114 takes an automated action. The action may be creating analarm and sending the alarm to one or more responsible parties, forexample a responsible cyber analyst or security administrator. In anembodiment, the alarms comprise a severity level from 1 to 5, where 5represents the highest severity (most dangerous or highest risk) cyberthreat. In another embodiment, the alarms comprise a severity level from1 to 3. In another embodiment, the alarms comprise a severity level from1 to 10. In another embodiment, a different scale of alarm severity maybe employed. In an embodiment, alarms may also comprise a priority, forexample a priority in which alarms may be automatically processed byalarm handling applications or scripts. Alarms may comprise theidentities of or links to the notifications 126 the analysis of whichtriggered the rules engine 114 to generate the alarm. Alarms maycomprise the identities and/or IP addresses of internal hosts 104 and/orcommunication equipment that are associated with the notifications 126analysis of which triggered the rules engine 114 to generate the alarm.The WS 128 may be used to review the content of the alarm, for exampleto review the notifications 126 that associate to the alarm.

Other actions that the rules engine 114 may take may comprise sandboxingan application executing on an internal host 104 or communicationequipment, restricting the operations accessible to the subject internalhost 104 or communication equipment (e.g., restrict transmission of datapackets to the external network 136 and/or to one of the external hosts138), suspending the application executing on the internal host 104 orcommunication equipment, or taking down the internal host 104 orcommunication equipment. Sandboxing refers to executing an applicationor software program in a controlled and/or restricted environment, toreduce the potential for the application or program to inflict harm onthe internal host 104, the LAN segment 102, and/or the enterprisenetwork domain 101. Sandboxing may be enforced by an operating system ofthe internal host 104 and may prevent the application executing in thesandbox from reading or writing to the LAN segment 102 and/or theenterprise network domain 101 or to read from or write to hostinterfaces of the internal host 104. Sandboxing may be a temporaryaction to allow cyber analysts time to determine whether the subjectapplication is compromised and/or poses a cyber threat to the internalhost 104, the LAN segment 106, and/or the enterprise network domain 101.Taking down the internal host 104 may mean disabling its host interfacesto the LAN segment 102 and/or to the enterprise network domain 101.Alternatively, taking down the internal host 104 may mean shutting thehost down and/or removing power to the host.

In some circumstances, the rules engine 114 may be triggered to analyzethe notifications 126 associated with a particular internal host 104 onthe event that a new notification 126 identifying the internal host 104is generated and stored in the data store 124. It is noted that thesensors 108, the threat sensors, and the flow sensor 116 may monitor thecommunication equipment comprising the internal network 130 as well asthe internal hosts 104 and generate notifications about thosecommunication equipment items also.

The rules engine 114 may generate an alarm when an internal host 104 oritem of communication equipment uses a communication protocol that it isnot expected to use, for example when a printer originates an FTPtransmission or originates an HTTP GET request. A plurality of rules maybe defined for the rules engine 114 identifying unlikely communicationevents for specific internal hosts 104 based on a narrowly circumscribedfunction performed by the internal host 104. The expected functionalroles of some of the internal hosts 104 may be defined and stored in thedata store 124. For example, an email server role may be defined for afirst internal host 104, a printing role may be defined for a secondinternal host 104, a human resources database management system role maybe defined for a third internal host 104, a customer billing functionmay be defined for a fourth internal host 104, and so on.

Some examples of cyber threat detection scenarios are described below topromote better appreciating the workings of the enterprise securitysystem. An HTTP message is transmitted by an internal host 104 that isdetermined by a sensor 108 to contain a hard coded IP address andfurther that the transmission of the HTTP message was not preceded by aDNS lookup that returned the hard coded, encapsulated IP address. Anotification 126 identifying this internal host 104, the hard coded IPaddress, and the identity of the anomalous communication event (HTTPmessage encapsulating a hard coded IP address when the IP address hadnot previously been resolved by an associated DNS lookup response) iscreated and stored in the data store 124. In response to the newnotification 126, the rules engine 114 searches the data store 124 forother notifications 126 associated with the subject internal host 104but finds none.

At a later time, the flow sensor 116 detects an increased volume ofoutbound data traffic from the internal host 104. For example, theoutbound TCP traffic of the internal host 104 is a standard deviationabove the median outbound TCP traffic for the TCP protocol on thesubject host interface of the internal host 104. A correspondingnotification 126 identifying this internal host 104, the address of thedestination of outbound TCP transmission, and the identity of theanomalous communication event is created and stored in the data store124. In response to the new notification 126, the rules engine searchesthe data store 124 for other notifications 126 and finds thenotification recording the communication event of the HTTP messageencapsulating the hard coded IP address. The rules engine 114 determinesthat the hard coded IP address is the same address as the destinationaddress associated with the new notification 126. This might besufficient to generate an alarm, but for purposes of example let it besupposed these two notifications are not determined to be alarmable.

At a yet later time, an updated threat list is received by the threatlist application 118 and a new IP address of an external host 138 ispresent in the black list. A new threat list sensor is instantiated ineach of the LAN segments 102 (or the single threat list sensor in eachLAN segment 102 is updated with the black listed IP address). Anotheroutbound TCP packet is detected by the threat list sensor for beingaddressed to a black listed IP address of an external host 138, in factthe recently identified as black listed external host. A notification126 is created indicating the internal host 104, the IP address of theexternal host 138, and the black listed transmission event, and thenotification 126 is stored in the data store 124. In response to the newnotification 126, the rules engine 114 reviews the two earliernotifications 126 related to the subject internal host 104 in the lightof the new notification 126. In the context of the information containedin the three notifications—each from a different variable or dimensionof the security system (i.e., from the network traffic monitoringdimension, from the network behavior analysis dimension, and from thethreat list dimension)—the rules engine 114 determines that thenotifications 126 match a threat pattern defined in one of its rules andgenerates an appropriate alarm and/or takes an appropriate correctiveaction, such as suspending the application executing on the internalhost 104.

As another example, Jane is the CEO at Acme Widgets and uses her laptopcomputer for business analytics and correspondence. Her laptop isrunning a suite of office software applications, has a firewall enabled,runs anti-virus software, and sits on the enterprise network domainjoined to Acme's Active Directory Domain Services (AD DS). Acme usesfirewalls, intrusion detection systems (IDSes), and sandboxingtechnology to weed out malicious attacks. The anti-virus software on herlaptop does a full signature-based scan for malware every day at 2:00AM.

While at the office, Jane receives an e-mail addressed directly to herthat purports to be from a shareholder requesting the latest 10-Qfiling. Attached to the e-mail is a standardized document format filethat, unbeknownst to Jane, carries a malicious trojan horse that uses adocument reader vulnerability to install itself on her laptop. Themalicious code itself, a BADBOY variant, is rather old, but it has beenmodified to avoid detection by current anti-virus products. BADBOY.vsuccessfully installs and becomes reboot-persistent through registry keyentries. BADBOY.v performs the following tasks: (1) downloads additionalcomponents directly from IP a.b.c.d; (2) installs these components onJane's laptop, one of which is a key logger; (3) directly connects to IPf.g.h.k for Command and Control; and (4) exfiltrates all key-loggedinformation to IP m.n.p.q in an encrypted format

In the two weeks between the installation of BADBOY.v on Jane's laptopand the development by the anti-virus development company of a BADBOY vsignature for use in extirpating the new variant of BADBOY, the BADBOY.vinstalled on Jane's laptop has attempted to reach out to the aboveseeded address, but the enterprise security system disclosed above hasdetected these attempts, has determined that they are direct requests toIP addresses without prior name resolution, and has sandboxed thoserequests. The Acme incident response team determines that Jane's laptopcomputer is infected, finds the malicious file, and restores Jane'slaptop from image.

Turning now to FIG. 2, details of the SCP server 106 are described. Inan embodiment the SCP server 106 comprises an SCP application 150, aspreviously mentioned above. The SCP server 106 further comprises thesensors 108 as described above. In an embodiment, the SCP server 106comprises an SCP traffic analysis application 152 and an SCP data store156.

The SCP traffic analysis application 152 may receive LAN segment datatraffic information from the sensors 108 and accumulate counts of datatraffic in a plurality of bins or collectors in the SCP data store 156.The SCP traffic analysis application 152 may perform some statisticalanalysis on the data traffic, for example calculating derived valuesbased on the raw accumulated counts. These may include ratios oftransmitted to received data packets and other ratios. In an embodiment,the SCP traffic analysis application 152 may be configured with expectedtraffic volume values and measures of traffic volume variability by thetraffic analysis application 120 executing on the computer system 112.The SCP traffic analysis application 152 may evaluate current trafficvolumes relative to the configured expected traffic volumes and measuresof traffic volume variability and generate notifications 126 (whentraffic varies by more than a predefined number of units of the measureof variability from the configured expected traffic volume value) thatare transmitted to the cyber threat data store 124. This alternativeapproach to comparing current traffic flows to expected traffic volumeand measures of traffic volume variability by the traffic analysisapplication 120 executing on the computer system 112 may reduce theprocessing load on the computer system 112 and promote more rapididentification of network data traffic anomalies.

The SCP traffic analysis application 152 forwards traffic data to bestored in the traffic statistics data store 129. This traffic data maybe in the form of raw counts in bins or collectors. Alternatively, thetraffic data sent to the traffic statistics data store 129 may comprisesummaries and/or derived values. In an embodiment, the SCP server 106further comprises an SCP rules engine 154 that may monitor notificationsgenerated by the sensors 108 and generate further notifications basedthereupon. Additionally, the SCP rules engine 154 may provide somepre-processing of some of the notifications output by the sensors 108before they are transmitted up to the cyber security data store 124. TheSCP rules engine 154 may provide a single notification that subsumes theinformation or the context of multiple notifications generated byindividual sensors 108.

Turning now to FIG. 3A and FIG. 3B, a method 200 is described. At block202, a computer system collects information on data packet traffic in anenterprise network during a plurality of monitoring periods. Forexample, the enterprise network data flow sensor 116 collectsinformation from each of the host interfaces of the internal hosts 104and the communication equipment composing the internal network 130. Inan embodiment, the information indicates what communication protocolsare used. At block 204, the computer system determines an expected datapacket flow rate and a measure for the data packet flow rate variabilityfor each of a plurality of host computers in the enterprise networkbased on the data packet traffic information. In an embodiment, thecomputer system determines an expected data packet flow rate and ameasure of data packet flow rate variability for each of the hostinterfaces for each different communication protocol used by the hostinterface for each of the internal hosts 104 and the communicationequipment composing the internal network 130. In an embodiment, theexpected values and measures of variability are calculated independentlyfor each of a plurality of different time periods, for example hourly,daily, and/or weekly.

At block 206, a flow sensor application executing on the computer systemdetermines data packet flow rates in the enterprise network. At block208, the flow sensor application determines that a data packet flow rateof a first host computer of the plurality of host computers in theenterprise network is excessive based on the expected data packet flowrate and the measure of the data packet flow rate variability associatedwith the first host computer. In an embodiment, the determination ismade based on comparison between the observed current data packet flowrate to an expected data packet flow rate defined for a like period oftime. In an embodiment, the determination is made by comparing the datapacket flow rate of a first host interface of the first host computer toa corresponding expected data packet flow rate and measure of datapacket flow rate variability determined for the first host interface ofthe first host computer. In an embodiment, the determination is made bycomparing the data packet flow rate of the first host computer during aspecific period of time to a corresponding expected value of and measureof variability of data packet flow rate for the first host computerdetermined for the corresponding period of time. In an embodiment, thedetermination is made by comparing the data packet flow rate of thefirst host computer of a specific communication protocol to acorresponding expected value and measure of variability of data packetflow rate of the same specific communication protocol determined for thefirst host computer.

At block 210, in response to the excessive data packet flow rate of thefirst host computer, the flow sensor application transmits a firstnotification to a cyber threat data store (e.g., data store 124), wherethe first notification comprises an identity of the flow sensorapplication as the sender, an identity of the first host computer, andan identity of a notification reason. At block 212, a cyber threat listapplication executing on the computer system reads a threat data list(e.g., reads the threat list data from the threat list data store 125),where the threat data list comprises a plurality of entries, each entryidentifying an external host computer located outside of the enterprisenetwork and metadata about the external host computer and the threat itposes. At block 214, configure a threat listed host sensor applicationwith threat list data from a threat list data store, where the threatlist data store comprises a list of threat entries, each entryidentifying an external host computer located outside of the enterprisenetwork and metadata about the external host computer and the threat itposes, wherein one of the entries identifies a first external hostcomputer;

At block 216, the threat listed host sensor application determines thatthe first external host computer sent a data packet to the first hostcomputer or that the first host computer sent a data packet to the firstexternal host computer. At block 218, responsive to determining a datapacket sent between the first external host computer and the first hostcomputer, the threat listed host sensor application transmits a secondnotification to the cyber threat data store, where the secondnotification comprises an identity of the threat listed host sensorapplication, an identity of the first external host computer, anidentity of the first computer, metadata about the first externalcomputer and about the threat it poses, and an identification of anotification reason.

At block 220, a rules engine application executing on the computersystem analyzes the notifications identifying the first host computer.At block 222, based on analyzing the notifications identifying the firsthost computer, the rules engine application sends an alarm to a userinterface. In an embodiment, the rules engine application may furthertake automated action to attenuate the cyber threat, for examplesandboxing an application executing on the first host computer,suspending execution of the application executing on the first hostcomputer, or shutting down the first host computer.

FIG. 4 illustrates a computer system 380 suitable for implementing oneor more embodiments disclosed herein. The computer system 380 includes aprocessor 382 (which may be referred to as a central processor unit orCPU) that is in communication with memory devices including secondarystorage 384, read only memory (ROM) 386, random access memory (RAM) 388,input/output (I/O) devices 390, and network connectivity devices 392.The processor 382 may be implemented as one or more CPU chips.

It is understood that by programming and/or loading executableinstructions onto the computer system 380, at least one of the CPU 382,the RAM 388, and the ROM 386 are changed, transforming the computersystem 380 in part into a particular machine or apparatus having thenovel functionality taught by the present disclosure. It is fundamentalto the electrical engineering and software engineering arts thatfunctionality that can be implemented by loading executable softwareinto a computer can be converted to a hardware implementation bywell-known design rules. Decisions between implementing a concept insoftware versus hardware typically hinge on considerations of stabilityof the design and numbers of units to be produced rather than any issuesinvolved in translating from the software domain to the hardware domain.Generally, a design that is still subject to frequent change may bepreferred to be implemented in software, because re-spinning a hardwareimplementation is more expensive than re-spinning a software design.Generally, a design that is stable that will be produced in large volumemay be preferred to be implemented in hardware, for example in anapplication specific integrated circuit (ASIC), because for largeproduction runs the hardware implementation may be less expensive thanthe software implementation. Often a design may be developed and testedin a software form and later transformed, by well-known design rules, toan equivalent hardware implementation in an application specificintegrated circuit that hardwires the instructions of the software. Inthe same manner as a machine controlled by a new ASIC is a particularmachine or apparatus, likewise a computer that has been programmedand/or loaded with executable instructions may be viewed as a particularmachine or apparatus.

Additionally, after the system 380 is turned on or booted, the CPU 382may execute a computer program or application. For example, the CPU 382may execute software or firmware stored in the ROM 386 or stored in theRAM 388. In some cases, on boot and/or when the application isinitiated, the CPU 382 may copy the application or portions of theapplication from the secondary storage 384 to the RAM 388 or to memoryspace within the CPU 382 itself, and the CPU 382 may then executeinstructions that the application is comprised of. In some cases, theCPU 382 may copy the application or portions of the application frommemory accessed via the network connectivity devices 392 or via the I/Odevices 390 to the RAM 388 or to memory space within the CPU 382, andthe CPU 382 may then execute instructions that the application iscomprised of. During execution, an application may load instructionsinto the CPU 382, for example load some of the instructions of theapplication into a cache of the CPU 382. In some contexts, anapplication that is executed may be said to configure the CPU 382 to dosomething, e.g., to configure the CPU 382 to perform the function orfunctions promoted by the subject application. When the CPU 382 isconfigured in this way by the application, the CPU 382 becomes aspecific purpose computer or a specific purpose machine.

The secondary storage 384 is typically comprised of one or more diskdrives or tape drives and is used for non-volatile storage of data andas an over-flow data storage device if RAM 388 is not large enough tohold all working data. Secondary storage 384 may be used to storeprograms which are loaded into RAM 388 when such programs are selectedfor execution. The ROM 386 is used to store instructions and perhapsdata which are read during program execution. ROM 386 is a non-volatilememory device which typically has a small memory capacity relative tothe larger memory capacity of secondary storage 384. The RAM 388 is usedto store volatile data and perhaps to store instructions. Access to bothROM 386 and RAM 388 is typically faster than to secondary storage 384.The secondary storage 384, the RAM 388, and/or the ROM 386 may bereferred to in some contexts as computer readable storage media and/ornon-transitory computer readable media.

I/O devices 390 may include printers, video monitors, liquid crystaldisplays (LCDs), touch screen displays, keyboards, keypads, switches,dials, mice, track balls, voice recognizers, card readers, paper tapereaders, or other well-known input devices.

The network connectivity devices 392 may take the form of modems, modembanks, Ethernet cards, universal serial bus (USB) interface cards,serial interfaces, token ring cards, fiber distributed data interface(FDDI) cards, wireless local area network (WLAN) cards, radiotransceiver cards that promote radio communications using protocols suchas code division multiple access (CDMA), global system for mobilecommunications (GSM), long-term evolution (LTE), worldwideinteroperability for microwave access (WiMAX), near field communications(NFC), radio frequency identity (RFID), and/or other air interfaceprotocol radio transceiver cards, and other well-known network devices.These network connectivity devices 392 may enable the processor 382 tocommunicate with the Internet or one or more intranets. With such anetwork connection, it is contemplated that the processor 382 mightreceive information from the network, or might output information to thenetwork in the course of performing the above-described method steps.Such information, which is often represented as a sequence ofinstructions to be executed using processor 382, may be received fromand outputted to the network, for example, in the form of a computerdata signal embodied in a carrier wave.

Such information, which may include data or instructions to be executedusing processor 382 for example, may be received from and outputted tothe network, for example, in the form of a computer data baseband signalor signal embodied in a carrier wave. The baseband signal or signalembedded in the carrier wave, or other types of signals currently usedor hereafter developed, may be generated according to several methodswell-known to one skilled in the art. The baseband signal and/or signalembedded in the carrier wave may be referred to in some contexts as atransitory signal.

The processor 382 executes instructions, codes, computer programs,scripts which it accesses from hard disk, floppy disk, optical disk(these various disk based systems may all be considered secondarystorage 384), flash drive, ROM 386, RAM 388, or the network connectivitydevices 392. While only one processor 382 is shown, multiple processorsmay be present. Thus, while instructions may be discussed as executed bya processor, the instructions may be executed simultaneously, serially,or otherwise executed by one or multiple processors. Instructions,codes, computer programs, scripts, and/or data that may be accessed fromthe secondary storage 384, for example, hard drives, floppy disks,optical disks, and/or other device, the ROM 386, and/or the RAM 388 maybe referred to in some contexts as non-transitory instructions and/ornon-transitory information.

In an embodiment, the computer system 380 may comprise two or morecomputers in communication with each other that collaborate to perform atask. For example, but not by way of limitation, an application may bepartitioned in such a way as to permit concurrent and/or parallelprocessing of the instructions of the application. Alternatively, thedata processed by the application may be partitioned in such a way as topermit concurrent and/or parallel processing of different portions of adata set by the two or more computers. In an embodiment, virtualizationsoftware may be employed by the computer system 380 to provide thefunctionality of a number of servers that is not directly bound to thenumber of computers in the computer system 380. For example,virtualization software may provide twenty virtual servers on fourphysical computers. In an embodiment, the functionality disclosed abovemay be provided by executing the application and/or applications in acloud computing environment. Cloud computing may comprise providingcomputing services via a network connection using dynamically scalablecomputing resources. Cloud computing may be supported, at least in part,by virtualization software. A cloud computing environment may beestablished by an enterprise and/or may be hired on an as-needed basisfrom a third party provider. Some cloud computing environments maycomprise cloud computing resources owned and operated by the enterpriseas well as cloud computing resources hired and/or leased from a thirdparty provider.

In an embodiment, some or all of the functionality disclosed above maybe provided as a computer program product. The computer program productmay comprise one or more computer readable storage medium havingcomputer usable program code embodied therein to implement thefunctionality disclosed above. The computer program product may comprisedata structures, executable instructions, and other computer usableprogram code. The computer program product may be embodied in removablecomputer storage media and/or non-removable computer storage media. Theremovable computer readable storage medium may comprise, withoutlimitation, a paper tape, a magnetic tape, magnetic disk, an opticaldisk, a solid state memory chip, for example analog magnetic tape,compact disk read only memory (CD-ROM) disks, floppy disks, jump drives,digital cards, multimedia cards, and others. The computer programproduct may be suitable for loading, by the computer system 380, atleast portions of the contents of the computer program product to thesecondary storage 384, to the ROM 386, to the RAM 388, and/or to othernon-volatile memory and volatile memory of the computer system 380. Theprocessor 382 may process the executable instructions and/or datastructures in part by directly accessing the computer program product,for example by reading from a CD-ROM disk inserted into a disk driveperipheral of the computer system 380. Alternatively, the processor 382may process the executable instructions and/or data structures byremotely accessing the computer program product, for example bydownloading the executable instructions and/or data structures from aremote server through the network connectivity devices 392. The computerprogram product may comprise instructions that promote the loadingand/or copying of data, data structures, files, and/or executableinstructions to the secondary storage 384, to the ROM 386, to the RAM388, and/or to other non-volatile memory and volatile memory of thecomputer system 380.

In some contexts, the secondary storage 384, the ROM 386, and the RAM388 may be referred to as a non-transitory computer readable medium or acomputer readable storage media. A dynamic RAM embodiment of the RAM388, likewise, may be referred to as a non-transitory computer readablemedium in that while the dynamic RAM receives electrical power and isoperated in accordance with its design, for example during a period oftime during which the computer system 380 is turned on and operational,the dynamic RAM stores information that is written to it. Similarly, theprocessor 382 may comprise an internal RAM, an internal ROM, a cachememory, and/or other internal non-transitory storage blocks, sections,or components that may be referred to in some contexts as non-transitorycomputer readable media or computer readable storage media.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods may beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted or not implemented.

Also, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as directly coupled or communicating witheach other may be indirectly coupled or communicating through someinterface, device, or intermediate component, whether electrically,mechanically, or otherwise. Other examples of changes, substitutions,and alterations are ascertainable by one skilled in the art and could bemade without departing from the spirit and scope disclosed herein.

What is claimed is:
 1. A cyber threat attenuation system, comprising: acyber threat data store; a threat list data store comprising a list ofthreat entries, each entry identifying an external host computer locatedoutside of the enterprise network and metadata about the external hostcomputer and the threat it poses; a user interface; a plurality ofsensor control points (SCPs), wherein at least one SCP is located ineach local area network (LAN) segment of an enterprise network, andwherein each SCP comprises a first processor, a first non-transitorymemory, and a plurality of sensor applications stored in the firstnon-transitory memory that each, when executed by the processor,analyzes data packets transported by the LAN segment in which the SCP islocated based on at least one criterion identified by the sensorapplication and, responsive to a data packet satisfying the at least onecriterion, transmits a notification identifying the sensor applicationas a transmitting sensor, an identity of the source of the data packet,an identity of the destination of the data packet, and a notificationreason to the cyber threat data store, wherein one of the plurality ofsensor applications comprises a threat listed host sensor application;and an analytics correlation system (ACS) comprising: a secondprocessor, a second non-transitory memory, a threat list applicationstored in the second non-transitory memory that, when executed by thesecond processor, configures a threat listed host sensor application ineach of the SCPs based on the entries in the threat list data store, anda rules engine application stored in the second non-transitory memorythat, when executed by the processor, analyzes a plurality ofnotifications from the threat list application on the analyticscorrelation system and one or more of the plurality of SCPs identifyinga first host computer including a notification from a threat listed hostsensor application that identifies the first host computer as thedestination for a data packet sent by or the source for a data packetsent to a first external host computer identified in the threat listhosted sensor application that created the notification, wherein thefirst host computer is one of a plurality of host computers in theenterprise network, and based on rules configured into the rules engineapplication and responsive to the analysis, transmits an alarm to theuser interface.
 2. The cyber threat attenuation system of claim 1,wherein the metadata about the external host computer in the entries inthe threat list data store comprises an identification of at least oneof a geographical location of an origin of a cyber threat and anindustry sector targeted by the cyber threat.
 3. The cyber threatattenuation system of claim 1, wherein the metadata about the externalhost computer in the entries in the threat list data store comprisesidentification of at least one of a system, a software, a firmware, oran application targeted by a cyber threat.
 4. The cyber threatattenuation system of claim 1, wherein the metadata about the externalhost computer in the entries in the threat list data store comprisesidentification of at least one of a date of the threat data or anexpiration date of the threat data.
 5. The cyber threat attenuationsystem of claim 1, wherein the rules engine application, based on theanalysis, sandboxes an application executing on the first host computer,restricts operations accessible to an application executing on the firsthost computer, suspends an application executing on the first hostcomputer, or shut downs the first host computer.
 6. The cyber threatattenuation system of claim 1, wherein the plurality of sensorapplications comprise at least one sensor application that analyzes datapackets at a network layer, at least one sensor application thatanalyzes data packets at a transport layer, and at least one sensorapplication that analyses data packets at a communication layer abovethe transport layer.
 7. The cyber threat attenuation system of claim 1,wherein the plurality of sensor applications comprise a statefulhypertext transfer protocol (HTTP) transaction sensor applicationconfigured to detect anomalous HTTP message sequences, an internetcontrol message protocol (ICMP) misuse sensor application configured todetect anomalous ICMP use, an address resolution protocol (ARP) misusesensor application configured to detect anomalous ARP use, and a domainname system (DNS) misuse sensor application configured to detectanomalous user datagram protocol (UDP) use.
 8. The cyber threatattenuation system of claim 1, further comprising a network data trafficdata store that stores expected values of data traffic volumes andmeasures of data traffic volume variabilities for hosts in each of aplurality of LAN segments of an enterprise network and stores host datatraffic volume information, and wherein the ACS further comprises anetwork data traffic flow sensor application stored in the secondnon-transitory memory that, when executed by the processor: accesses,from the network data traffic data store, information on data packettraffic in the enterprise network, inside a firewall connection of theenterprise network to the external network, during a monitoring period,analyzes the host data packet traffic information to determine, based oncomparing the data traffic to expected values of data traffic volumesand measures of data traffic volume variabilities, an unusual datapacket traffic associated with a host computer in the enterprisenetwork, inside the firewall connection of the enterprise network to theexternal network, and transmits a notification comprising informationabout the unusual data packet traffic and an identity of the hostcomputer associated with the unusual data packet traffic to the cyberthreat data store.
 9. The cyber threat attenuation system of claim 1,wherein the at least one criterion is of a plurality of criteriaidentified by the plurality of sensor applications, and wherein theplurality of criteria comprises at least two of sensing time to livevalues in excess of a threshold, sensing specific sequences of statetransitions, sensing sizes of data communication in excess of athreshold, sensing predefined port numbers or ranges of port numbers,sensing predefined names or suffixes, sensing file transfercommunication events that violate file transfer rules, sensing anomaloustransport control protocol (TCP) message patterns, sensing anomaloushypertext transfer protocol (HTTP) message sequences, sensing anomalousInternet Control Message Protocol (ICMP) use, sensing anomalous addressresolution protocol (ARP) use, sensing anomalous user datagram protocol(UDP) use, sensing anomalous domain name system (DNS) use, sensing thatscanning is being attempted using TCP or UDP packets, sensing internalhosts connecting to or being connected from one of a predefined list ofports, sensing anomalous server message block (SMB) messaging patterns,and sensing anomalous remote desktop protocol (RDP) messaging patterns.10. The cyber threat attenuation system of claim 1, wherein a new sensorapplication is developed and deployed to at least one of the pluralityof SCPs in response to a new cyber threat vulnerability beingidentified.
 11. A method of attenuating cyber threats, comprising: foreach of a plurality of sensor control points (SCPs), analyzing, by eachsensor application of a plurality of sensor applications executing in alocal area network (LAN) segment of an enterprise network, data packetstransmitted on the LAN segment based on at least one criterionidentified by the sensor application, wherein at least one SCP islocated in each local area network (LAN) segment of the enterprisenetwork; responsive to a data packet satisfying the at least onecriterion, transmitting, by the sensor application, a notificationidentifying the sensor application as a transmitting sensor, an identityof the source of the data packet, an identity of the destination of thedata packet, and a notification reason to a cyber threat data store,wherein one of the plurality of sensor applications comprises a threatlisted host sensor application; configuring, by a threat listapplication executing stored in a non-transitory memory and executed bya processor of a computer system, a threat listed host sensorapplication in each of the SCPs based on threat list data from a threatlist data store, where the threat list data store comprises a list ofthreat entries, each entry identifying an external host computer locatedoutside of the enterprise network and metadata about the external hostcomputer and the threat it poses; analyzing, by a rules engineapplication stored in a non-transitory memory and executed by aprocessor of the computer system, a plurality of notifications from thethreat list application and one or more of the plurality of SCPsidentifying a first host computer including a notification from a threatlisted host sensor application that identifies the first host computeras the destination for a data packet sent by or the source for a datapacket sent to a first external host computer identified in the threatlist hosted sensor application that created the notification, whereinthe first host computer is one of a plurality of host computers in theenterprise network; and based on rules configured into the rules engineapplication and responsive to the analysis, transmitting, by the rulesengine application, an alarm to a user interface.
 12. The method ofclaim 11, wherein the metadata about the external host computer in theentries in the threat list data store comprises an identification of atleast one of a geographical location of an origin of a cyber threat andan industry sector targeted by the cyber threat.
 13. The method of claim11, wherein the metadata about the external host computer in the entriesin the threat list data store comprises identification of at least oneof a system, a software, a firmware, or an application targeted by acyber threat.
 14. The method of claim 11, wherein the metadata about theexternal host computer in the entries in the threat list data storecomprises identification of at least one of a date of the threat data oran expiration date of the threat data.
 15. The method of claim 11,further comprising, based on the analysis, sandboxing, by the rulesengine application, an application executing on the first host computer,restricting, by the rules engine application, operations accessible toan application executing on the first host computer, suspending, by therules engine application, an application executing on the first hostcomputer, or shutting down, by the rules engine application, the firsthost computer.
 16. The method of claim 11, wherein the plurality ofsensor applications comprise at least one sensor application thatanalyzes data packets at a network layer, at least one sensorapplication that analyzes data packets at a transport layer, and atleast one sensor application that analyses data packets at acommunication layer above the transport layer.
 17. The method of claim11, wherein the plurality of sensor applications comprise a statefulhypertext transfer protocol (HTTP) transaction sensor applicationconfigured to detect anomalous HTTP message sequences, an internetcontrol message protocol (ICMP) misuse sensor application configured todetect anomalous ICMP use, an address resolution protocol (ARP) misusesensor application configured to detect anomalous ARP use, and a domainname system (DNS) misuse sensor application configured to detectanomalous user datagram protocol (UDP) use.
 18. The method of claim 11,further comprising: accessing, by a network data traffic flow sensorapplication stored in a non-transitory memory and executed by aprocessor of the computer system, from a network data traffic datastore, information on data packet traffic in the enterprise network,inside a firewall connection of the enterprise network to the externalnetwork, during a monitoring period, where in the network data trafficdata store stores expected values of data traffic volumes and measuresof data traffic volume variabilities for hosts in each of a plurality ofLAN segments of the enterprise network and stores host data trafficvolume information; analyzing, by the network data traffic flow sensorapplication, the host data packet traffic information to determine,based on comparing the data traffic to expected values of data trafficvolumes and measures of data traffic volume variabilities, an unusualdata packet traffic associated with a host computer in the enterprisenetwork, inside the firewall connection of the enterprise network to theexternal network; and transmitting, by the network data traffic flowsensor application, a notification comprising information about theunusual data packet traffic and an identity of the host computerassociated with the unusual data packet traffic to the cyber threat datastore.
 19. The method of claim 11, wherein the at least one criterion isof a plurality of criteria identified by the plurality of sensorapplications, and wherein the plurality of criteria comprises at leasttwo of sensing time to live values in excess of a threshold, sensingspecific sequences of state transitions, sensing sizes of datacommunication in excess of a threshold, sensing predefined port numbersor ranges of port numbers, sensing predefined names or suffixes, sensingfile transfer communication events that violate file transfer rules,sensing anomalous transport control protocol (TCP) message patterns,sensing anomalous hypertext transfer protocol (HTTP) message sequences,sensing anomalous Internet Control Message Protocol (ICMP) use, sensinganomalous address resolution protocol (ARP) use, sensing anomalous userdatagram protocol (UDP) use, sensing anomalous domain name system (DNS)use, sensing that scanning is being attempted using TCP or UDP packets,sensing internal hosts connecting to or being connected from one of apredefined list of ports, sensing anomalous server message block (SMB)messaging patterns, and sensing anomalous remote desktop protocol (RDP)messaging patterns.
 20. The method of claim 11, further comprisingdeploying a newly developed sensor application to at least one of theplurality of SCPs in response to a new cyber threat vulnerability beingidentified.